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Developments and further applications of ephemeral data derived potentials

Pascal Thomas Salzbrenner1*, Se Hun Joo1, Lewis J Conway1,2, Peter I C Cooke1, Bonan Zhu3, Milosz P Matraszek4, William Charles Witt1, Chris J Pickard1,2*

1 Department of Materials Science & Metallurgy, University of Cambridge, Cambridge, U.K.

2 Advanced Institute for Materials Research, Tohoku University, Sendai, Japan

3 Department of Chemistry, University College London, London, U.K.

4 Trinity College, University of Cambridge, Cambridge, U.K.

* Corresponding authors emails: pts28@cam.ac.uk, cjp20@cam.ac.uk
DOI10.24435/materialscloud:44-c5 [version v1]

Publication date: Sep 28, 2023

How to cite this record

Pascal Thomas Salzbrenner, Se Hun Joo, Lewis J Conway, Peter I C Cooke, Bonan Zhu, Milosz P Matraszek, William Charles Witt, Chris J Pickard, Developments and further applications of ephemeral data derived potentials, Materials Cloud Archive 2023.150 (2023), doi: 10.24435/materialscloud:44-c5.


Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. This record provides the data associated with the different case studies demonstrating the uses of ephemeral data-derived potentials in the article "Developments and Further Applications of Ephemeral Data Derived Potentials".

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machine-learned interatomic potentials molecular dynamics phonons phase diagram superionicity hydrides metal-organic frameworks

Version history:

2023.150 (version v1) [This version] Sep 28, 2023 DOI10.24435/materialscloud:44-c5